A mature, stable thinking-oriented flagship with a 1M context
No longer the newest generation, but a practical pick when you value reliability and long context over frontier scores.
Overview
Gemini 2.5 Pro is Google DeepMind's thinking-oriented reasoning model, released on March 25, 2025. At launch it topped the LMArena human-preference leaderboard and became the flagship of the Gemini 2.5 family, pairing deliberate "think first, then answer" reasoning with a native multimodal foundation for text, image, audio, and video input.
It is no longer Google's newest generation — Gemini 3 pushes further on speed and hard-problem density — but it remains a mature, stable, high-context reasoning model. For production systems that don't need the absolute frontier yet value reliability and a 1M-token window, 2.5 Pro is still a practical choice.
Key capabilities
| Dimension | Detail |
|---|---|
| Context window | 1,048,576 tokens (about 1M) |
| Max output | 65,536 tokens (about 64K) |
| Input modalities | Text, image (Google's native model also supports audio and video) |
| Output modalities | Text |
| Tools | function calling, structured outputs, streaming, thinking, code execution |
Gemini Pro-family requests enter a higher tier when single-request input exceeds 200K tokens (roughly 2× input and 1.5× output multipliers). This is a product pricing structure, not a per-unit price; see live rates in the model catalog.
Benchmarks
Gemini 2.5 Pro's evaluation spine is math / science reasoning plus software engineering, and those results were obtained without expensive majority-voting test-time tricks.
Front-rank math / science reasoning for early 2025
All scores achieved without expensive test-time tricks such as majority voting.
On math, AIME 2024 92.0% and AIME 2025 86.7%; on science, GPQA Diamond 84.0% — front-rank reasoning for early 2025. The clearest "ceiling" signal is Humanity's Last Exam 18.8%, which was the no-tools SOTA at the time, showing it had real footing on the hardest cross-disciplinary academic questions. Combined with topping LMArena at launch, 2.5 Pro earned best-in-generation marks for both accuracy and answer quality.
Software engineering and long context
Capable of real repo fixes and long-document retrieval
SWE-bench Verified uses a custom agent setup; the 1M window supports cross-section retrieval and synthesis.
SWE-bench Verified 63.8% (in a custom agent setup) is already enough to handle real-repo bug fixes. With a 1M-token window, 2.5 Pro can ingest a full long document, a mid-size codebase, or a long conversation history in a single request, then reason and retrieve across sections. Google also emphasized visually polished web-app generation and agentic coding, which is useful when requirements, design context, and existing code need to be reasoned over together.
When to use it
- Complex reasoning and STEM work: multi-step math, science, and engineering problems where thinking improves stability.
- Long-document and mid-size repo analysis: 1M context for cross-section retrieval and synthesis.
- Web and agentic code: move from requirements to runnable frontends with code execution and tools.
- Stable reasoning at controlled cost: choose it when newest-generation capability is not required.
CrossModel exposes Gemini 2.5 Pro through an OpenAI-compatible /v1/chat/completions API. Current pricing is available in the model catalog.